Investigating a novel recombinant antibody to attenuate prostate cancer progression by targeting cell surface GRP78.
Bibliographic record
Abstract
206 Background: Prostate cancer (PCa) is characterized by increased activation of the procoagulant protein, tissue factor (TF) that drive tumour progression. We now show that this process is modulated by GRP78 on the cell surface (cs). In PCa GRP78 is a endoplasmic reticulum-resident chaperone that localizes to the cell surface where it functions as a signaling molecule with antigenic properties. In response to csGRP78 presentation, PCa patients produce autoantibodies (AutoAbs) against the N-terminus of GRP78. AutoAbs:csGRP78 complex acts as a potent driver of tumor growth via upregulation of the unfolded protein response (UPR) and TF activity. We hypothesize that inhibiting the binding of anti-GRP78 AutoAbs to csGRP78 will supress UPR and TF activity. Here we describe a recombinant anti-GRP78 antibody (AEP8587) that competes with the binding of AutoAbs to csGRP78 and may act as a novel therapeutic antibody with antitumor activity. Methods: Changes in TF activity or UPR markers were evaluated in vitro in the PCa cell line DU145 following treatment with anti-GRP78 AutoAbs or co-treatment with either enoxaparin, a low molecular weight heparin (LMWH), or AEP8587. Protein expression of TF and UPR markers was determined using western blotting and qRT-PCR. TF activity was determined using a real-time continuous assay. AutoAbs were purified PCa patients (St. Joseph’s Healthcare Hamilton). Results: Pre-prostatectomy PCa patients display high levels of anti-GRP78 AutoAbs (~60µg/ml), compared to healthy controls (~5µg/ml). Here, we show that anti-GRP78 AutoAb increases TF activation in vitro and leads to increased tumor progression in a DU145 xenograft model. In contrast, we show a co-treatment of anti-GRP78 AutoAb with either enoxaparin or AEP8587 completely abolishes the AutoAb-mediated increase in TF activity in vitro. Enoxaparin or AEP8587 co-treatment reversed the AutoAb effect on increased UPR markers. Conclusions: We have identified anti-GRP78 AutoAb as a driver of PCa progression. Our results indicate that a recombinant antibody, AEP8587, can bind to csGRP78 and prevent the binding of anti-GRP78 AutoAbs. This represents a potential novel means to manage PCa progression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".